{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:08:10Z","timestamp":1775912890700,"version":"3.50.1"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306224"],"award-info":[{"award-number":["62306224"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62471371"],"award-info":[{"award-number":["62471371"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong High-level Innovation Research Institution","award":["2021B0909050008"],"award-info":[{"award-number":["2021B0909050008"]}]},{"name":"Guangzhou Key Research and Development Program","award":["202206030003"],"award-info":[{"award-number":["202206030003"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["YJSJ25014"],"award-info":[{"award-number":["YJSJ25014"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Xidian-UTAR China Malaysia Science and Technology Institute-the Fundamental Research Funds for the Central Universities","award":["QTZX24096"],"award-info":[{"award-number":["QTZX24096"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1109\/tkde.2025.3581963","type":"journal-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T13:29:06Z","timestamp":1750685346000},"page":"4935-4947","source":"Crossref","is-referenced-by-count":7,"title":["A Universal Subhypergraph-Assisted Embedding Framework for Both Homogeneous and Heterogeneous Networks"],"prefix":"10.1109","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7907-7178","authenticated-orcid":false,"given":"Shibing","family":"Mo","sequence":"first","affiliation":[{"name":"School of Artifcial Intelligence and the Guangzhou Institute of Technology, Xidian University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5964-0123","authenticated-orcid":false,"given":"Xiangyi","family":"Teng","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1852-6364","authenticated-orcid":false,"given":"Kai","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Artifcial Intelligence, Xidian University, Xian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6834-5350","authenticated-orcid":false,"given":"Jing","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artifcial Intelligence and the Guangzhou Institute of Technology, Xidian University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4163-5577","authenticated-orcid":false,"given":"Kaixin","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Artifcial Intelligence, Xidian University, Xian, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109042"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3011866"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3106804"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3087791"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3151618"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS47720.2021.9553449"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-021-01251-0"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz718"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref10","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018"},{"key":"ref11","article-title":"Language models are few-shot learners","author":"Mann","year":"2020"},{"key":"ref12","article-title":"A survey of large language models","author":"Zhao","year":"2023"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2025.3548729"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/a16030126"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583403"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583464"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2453956"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.soc.27.1.415"},{"key":"ref19","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"ref20","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref21","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07862-6"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.399"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570484"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3672024"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11747"},{"key":"ref30","first-page":"8017","article-title":"Subgraph neural networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Alsentzer"},{"key":"ref31","first-page":"5862","article-title":"Graph meta learning via local subgraphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Huang"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449822"},{"key":"ref33","first-page":"18779","article-title":"SHINE: SubHypergraph inductive neural network","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Luo"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2021.100390"},{"key":"ref35","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yang"},{"key":"ref36","article-title":"Pitfalls of graph neural network evaluation","author":"Shchur","year":"2018"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380297"},{"key":"ref38","first-page":"21","article-title":"MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Abu-El-Haija"},{"key":"ref39","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref40","article-title":"New benchmarks for learning on non-homophilous graphs","author":"Lim","year":"2021"},{"key":"ref41","article-title":"Predict then propagate: Graph neural networks meet personalized PageRank","author":"Gasteiger","year":"2018"},{"key":"ref42","article-title":"Adaptive universal generalized PageRank graph neural network","author":"Chien","year":"2020"},{"key":"ref43","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhu"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16514"},{"key":"ref45","article-title":"Geom-GCN: Geometric graph convolutional networks","author":"Pei","year":"2020"},{"key":"ref46","first-page":"14239","article-title":"BernNet: Learning arbitrary graph spectral filters via Bernstein approximation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"He"},{"key":"ref47","first-page":"1362","article-title":"Revisiting heterophily for graph neural networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Luan"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615195"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.5220\/0012321400003636"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i18.34146"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098036"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330961"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467415"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3101356"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467350"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119982"},{"key":"ref59","article-title":"Classic GNNs are strong baselines: Reassessing GNNs for node classification","author":"Luo","year":"2024"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/69\/11119623\/11045888.pdf?arnumber=11045888","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T04:36:01Z","timestamp":1754627761000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11045888\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":59,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2025.3581963","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9]]}}}